{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "-knK4sZodDZg"
      },
      "source": [
        "##### Copyright 2018 The TensorFlow Probability Authors.\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "form",
        "colab": {},
        "colab_type": "code",
        "id": "zAM8G9A4dF4R"
      },
      "outputs": [],
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\"); { display-mode: \"form\" }\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "cPw5xFcq1kpw"
      },
      "source": [
        "# FFJORD\n",
        "\n",
        "\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca target=\"_blank\" href=\"https://www.tensorflow.org/probability/examples/FFJORD_Demo\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" /\u003eView on TensorFlow.org\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/FFJORD_Demo.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/FFJORD_Demo.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca href=\"https://storage.googleapis.com/tensorflow_docs/probability/tensorflow_probability/examples/jupyter_notebooks/FFJORD_Demo.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /\u003eDownload notebook\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "\u003c/table\u003e"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "BzmOdHFzXwPn"
      },
      "source": [
        "## Setup\n",
        "\n",
        "First install packages used in this demo."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "osWMEZ53VSJw"
      },
      "outputs": [],
      "source": [
        "!pip install -q dm-sonnet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "both",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "colab_type": "code",
        "id": "hC2H2qjyVuS-",
        "outputId": "06fdc154-1533-495e-a59f-eb948724685a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ]
        }
      ],
      "source": [
        "#@title Imports (tf, tfp with adjoint trick, etc)\n",
        "\n",
        "import numpy as np\n",
        "import tqdm as tqdm\n",
        "import sklearn.datasets as skd\n",
        "\n",
        "# visualization\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from scipy.stats import kde\n",
        "\n",
        "# tf and friends\n",
        "import tensorflow.compat.v2 as tf\n",
        "import tensorflow_probability as tfp\n",
        "import sonnet as snt\n",
        "tf.enable_v2_behavior()\n",
        "\n",
        "tfb = tfp.bijectors\n",
        "tfd = tfp.distributions\n",
        "\n",
        "def make_grid(xmin, xmax, ymin, ymax, gridlines, pts):\n",
        "  xpts = np.linspace(xmin, xmax, pts)\n",
        "  ypts = np.linspace(ymin, ymax, pts)\n",
        "  xgrid = np.linspace(xmin, xmax, gridlines)\n",
        "  ygrid = np.linspace(ymin, ymax, gridlines)\n",
        "  xlines = np.stack([a.ravel() for a in np.meshgrid(xpts, ygrid)])\n",
        "  ylines = np.stack([a.ravel() for a in np.meshgrid(xgrid, ypts)])\n",
        "  return np.concatenate([xlines, ylines], 1).T\n",
        "\n",
        "grid = make_grid(-3, 3, -3, 3, 4, 100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "form",
        "colab": {},
        "colab_type": "code",
        "id": "2S7iVERbVNCF"
      },
      "outputs": [],
      "source": [
        "#@title Helper functions for visualization\n",
        "def plot_density(data, axis):\n",
        "  x, y = np.squeeze(np.split(data, 2, axis=1))\n",
        "  levels = np.linspace(0.0, 0.75, 10)\n",
        "  kwargs = {'levels': levels}\n",
        "  return sns.kdeplot(x, y, cmap=\"viridis\", shade=True, \n",
        "                     shade_lowest=True, ax=axis, **kwargs)\n",
        "\n",
        "\n",
        "def plot_points(data, axis, s=10, color='b', label=''):\n",
        "  x, y = np.squeeze(np.split(data, 2, axis=1))\n",
        "  axis.scatter(x, y, c=color, s=s, label=label)\n",
        "\n",
        "\n",
        "def plot_panel(\n",
        "    grid, samples, transformed_grid, transformed_samples,\n",
        "    dataset, axarray, limits=True):\n",
        "  if len(axarray) != 4:\n",
        "    raise ValueError('Expected 4 axes for the panel')\n",
        "  ax1, ax2, ax3, ax4 = axarray\n",
        "  plot_points(data=grid, axis=ax1, s=20, color='black', label='grid')\n",
        "  plot_points(samples, ax1, s=30, color='blue', label='samples')\n",
        "  plot_points(transformed_grid, ax2, s=20, color='black', label='ode(grid)')\n",
        "  plot_points(transformed_samples, ax2, s=30, color='blue', label='ode(samples)')\n",
        "  ax3 = plot_density(transformed_samples, ax3)\n",
        "  ax4 = plot_density(dataset, ax4)\n",
        "  if limits:\n",
        "    set_limits([ax1], -3.0, 3.0, -3.0, 3.0)\n",
        "    set_limits([ax2], -2.0, 3.0, -2.0, 3.0)\n",
        "    set_limits([ax3, ax4], -1.5, 2.5, -0.75, 1.25)\n",
        "\n",
        "\n",
        "def set_limits(axes, min_x, max_x, min_y, max_y):\n",
        "  if isinstance(axes, list):\n",
        "    for axis in axes:\n",
        "      set_limits(axis, min_x, max_x, min_y, max_y)\n",
        "  else:\n",
        "    axes.set_xlim(min_x, max_x)\n",
        "    axes.set_ylim(min_y, max_y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "2DELBFg0WsLC"
      },
      "source": [
        "# FFJORD bijector\n",
        "In this colab we demonstrate FFJORD bijector, originally proposed in the paper by Grathwohl, Will, et al. [arxiv link](https://arxiv.org/pdf/1810.01367.pdf).\n",
        "\n",
        "In the nutshell the idea behind such approach is to establish a correspondence between a known **base distribution** and the **data distribution**.\n",
        "\n",
        "To establish this connection, we need to\n",
        "\n",
        "  1. Define a bijective map $\\mathcal{T}_{\\theta}:\\mathbf{x} \\rightarrow \\mathbf{y}$, $\\mathcal{T}_{\\theta}^{1}:\\mathbf{y} \\rightarrow \\mathbf{x}$ between the space $\\mathcal{Y}$ on which **base distribution** is defined and space $\\mathcal{X}$ of the data domain.\n",
        "  2. Efficiently keep track of the deformations we perform to transfer the notion of probability onto $\\mathcal{X}$.\n",
        "\n",
        "The second condition is formalized in the following expression for probability\n",
        "distribution defined on $\\mathcal{X}$:\n",
        "\n",
        "$$\n",
        "\\log p_{\\mathbf{x}}(\\mathbf{x})=\\log p_{\\mathbf{y}}(\\mathbf{y})-\\log \\operatorname{det}\\left|\\frac{\\partial \\mathcal{T}_{\\theta}(\\mathbf{y})}{\\partial \\mathbf{y}}\\right|\n",
        "$$\n",
        "\n",
        "FFJORD bijector accomplishes this by defining a transformation \n",
        "$$\n",
        "\\mathcal{T_{\\theta}}: \\mathbf{x} = \\mathbf{z}(t_{0}) \\rightarrow \\mathbf{y} = \\mathbf{z}(t_{1}) \\quad : \\quad \\frac{d \\mathbf{z}}{dt} = \\mathbf{f}(t, \\mathbf{z}, \\theta)\n",
        "$$\n",
        "\n",
        "This transformation is invertible, as long as function $\\mathbf{f}$ describing the evolution of the state $\\mathbf{z}$ is well behaved and the `log_det_jacobian` can be calculated by integrating the following expression.\n",
        "\n",
        "$$\n",
        "\\log \\operatorname{det}\\left|\\frac{\\partial \\mathcal{T}_{\\theta}(\\mathbf{y})}{\\partial \\mathbf{y}}\\right| = \n",
        "-\\int_{t_{0}}^{t_{1}} \\operatorname{Tr}\\left(\\frac{\\partial \\mathbf{f}(t, \\mathbf{z}, \\theta)}{\\partial \\mathbf{z}(t)}\\right) d t\n",
        "$$\n",
        "\n",
        "In this demo we will train a FFJORD bijector to warp a gaussian distribution onto the distribution defined by `moons` dataset. This will be done in 3 steps:\n",
        "  * Define **base distribution**\n",
        "  * Define FFJORD bijector\n",
        "  * Minimize exact log-likelihood of the dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Ls2u6WGNWvYB"
      },
      "source": [
        "First, we load the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "colab_type": "code",
        "id": "aWk3YD4zWx_b",
        "outputId": "2de3d328-ce1d-42e9-f43b-b1ee61b96f8c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "\u003cFigure size 576x576 with 1 Axes\u003e"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#@title Dataset\n",
        "DATASET_SIZE = 1024 * 8  #@param\n",
        "BATCH_SIZE = 256  #@param\n",
        "SAMPLE_SIZE = DATASET_SIZE\n",
        "\n",
        "moons = skd.make_moons(n_samples=DATASET_SIZE, noise=.06)[0]\n",
        "\n",
        "moons_ds = tf.data.Dataset.from_tensor_slices(moons.astype(np.float32))\n",
        "moons_ds = moons_ds.prefetch(tf.data.experimental.AUTOTUNE)\n",
        "moons_ds = moons_ds.cache()\n",
        "moons_ds = moons_ds.shuffle(DATASET_SIZE)\n",
        "moons_ds = moons_ds.batch(BATCH_SIZE)\n",
        "\n",
        "plt.figure(figsize=[8, 8])\n",
        "plt.scatter(moons[:, 0], moons[:, 1])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "JsmkbfCdWyFK"
      },
      "source": [
        "Next, we instantiate a base distribution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "knDCafOQWyKJ"
      },
      "outputs": [],
      "source": [
        "base_loc = np.array([0.0, 0.0]).astype(np.float32)\n",
        "base_sigma = np.array([0.8, 0.8]).astype(np.float32)\n",
        "base_distribution = tfd.MultivariateNormalDiag(base_loc, base_sigma)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "3m0IOnGoWyNp"
      },
      "source": [
        "We use a multi-layer perceptron to model `state_derivative_fn`.\n",
        "\n",
        "While not necessary for this dataset, it is often benefitial to make `state_derivative_fn` dependent on time. Here we achieve this by concatenating `t` to inputs of our network."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "Rq6mHVh8WyQZ"
      },
      "outputs": [],
      "source": [
        "class MLP_ODE(snt.Module):\n",
        "  \"\"\"Multi-layer NN ode_fn.\"\"\"\n",
        "  def __init__(self, num_hidden, num_layers, num_output, name='mlp_ode'):\n",
        "    super(MLP_ODE, self).__init__(name=name)\n",
        "    self._num_hidden = num_hidden\n",
        "    self._num_output = num_output\n",
        "    self._num_layers = num_layers\n",
        "    self._modules = []\n",
        "    for _ in range(self._num_layers - 1):\n",
        "      self._modules.append(snt.Linear(self._num_hidden))\n",
        "      self._modules.append(tf.math.tanh)\n",
        "    self._modules.append(snt.Linear(self._num_output))\n",
        "    self._model = snt.Sequential(self._modules)\n",
        "\n",
        "  def __call__(self, t, inputs):\n",
        "    inputs = tf.concat([tf.broadcast_to(t, inputs.shape), inputs], -1)\n",
        "    return self._model(inputs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "vXeBR0bTWyS5"
      },
      "outputs": [],
      "source": [
        "#@title Model and training parameters\n",
        "LR = 1e-2  #@param\n",
        "NUM_EPOCHS = 80  #@param\n",
        "STACKED_FFJORDS = 4  #@param\n",
        "NUM_HIDDEN = 8  #@param\n",
        "NUM_LAYERS = 3  #@param\n",
        "NUM_OUTPUT = 2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "FWvaiMDMWyVZ"
      },
      "source": [
        "Now we construct a stack of FFJORD bijectors. Each bijector is provided with `ode_solve_fn` and `trace_augmentation_fn` and it's own `state_derivative_fn` model, so that they represent a sequence of different transformations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "lPgF4uu-XNM1"
      },
      "outputs": [],
      "source": [
        "#@title Building bijector\n",
        "solver = tfp.math.ode.DormandPrince(atol=1e-5)\n",
        "ode_solve_fn = solver.solve\n",
        "trace_augmentation_fn = tfb.ffjord.trace_jacobian_exact\n",
        "\n",
        "bijectors = []\n",
        "for _ in range(STACKED_FFJORDS):\n",
        "  mlp_model = MLP_ODE(NUM_HIDDEN, NUM_LAYERS, NUM_OUTPUT)\n",
        "  next_ffjord = tfb.FFJORD(\n",
        "      state_time_derivative_fn=mlp_model,ode_solve_fn=ode_solve_fn,\n",
        "      trace_augmentation_fn=trace_augmentation_fn)\n",
        "  bijectors.append(next_ffjord)\n",
        "\n",
        "stacked_ffjord = tfb.Chain(bijectors[::-1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "nxPYgppYXNRk"
      },
      "source": [
        "Now we can use `TransformedDistribution` which is the result of warping `base_distribution` with `stacked_ffjord` bijector."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "R5aUlnDrXNU7"
      },
      "outputs": [],
      "source": [
        "transformed_distribution = tfd.TransformedDistribution(\n",
        "    distribution=base_distribution, bijector=stacked_ffjord)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "kpXxHaKJXNYL"
      },
      "source": [
        "Now we define our training procedure. We simply minimize negative log-likelihood of the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "y8YC4qpTXXj2"
      },
      "outputs": [],
      "source": [
        "#@title Training\n",
        "@tf.function\n",
        "def train_step(optimizer, target_sample):\n",
        "  with tf.GradientTape() as tape:\n",
        "    loss = -tf.reduce_mean(transformed_distribution.log_prob(target_sample))\n",
        "  variables = tape.watched_variables()\n",
        "  gradients = tape.gradient(loss, variables)\n",
        "  optimizer.apply(gradients, variables)\n",
        "  return loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "sQ1J7NYxXXnk"
      },
      "outputs": [],
      "source": [
        "#@title Samples\n",
        "@tf.function\n",
        "def get_samples():\n",
        "  base_distribution_samples = base_distribution.sample(SAMPLE_SIZE)\n",
        "  transformed_samples = transformed_distribution.sample(SAMPLE_SIZE)\n",
        "  return base_distribution_samples, transformed_samples\n",
        "\n",
        "\n",
        "@tf.function\n",
        "def get_transformed_grid():\n",
        "  transformed_grid = stacked_ffjord.forward(grid)\n",
        "  return transformed_grid"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Gbupg1AIXXrb"
      },
      "source": [
        "Plot samples from base and transformed distributions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "colab_type": "code",
        "id": "48XDLdQEXNbU",
        "outputId": "8e81f4f7-441b-4112-cc01-1325d340458c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:1817: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n"
          ]
        }
      ],
      "source": [
        "evaluation_samples = []\n",
        "base_samples, transformed_samples = get_samples()\n",
        "transformed_grid = get_transformed_grid()\n",
        "evaluation_samples.append((base_samples, transformed_samples, transformed_grid))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "colab_type": "code",
        "id": "CQyH94U1Xh_O",
        "outputId": "1aa753f0-f4b4-493c-fa9d-4394d6ccd912"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "\u003cFigure size 1152x432 with 4 Axes\u003e"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "panel_id = 0\n",
        "panel_data = evaluation_samples[panel_id]\n",
        "fig, axarray = plt.subplots(\n",
        "  1, 4, figsize=(16, 6))\n",
        "plot_panel(\n",
        "    grid, panel_data[0], panel_data[2], panel_data[1], moons, axarray, False)\n",
        "plt.tight_layout()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "colab_type": "code",
        "id": "qo9QhkVpXiGn",
        "outputId": "9dc4e2f3-e1f5-46d7-d586-b841505758cc"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  0%|          | 0/40 [00:00\u003c?, ?it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_probability/python/math/ode/base.py:350: calling while_loop_v2 (from tensorflow.python.ops.control_flow_ops) with back_prop=False is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "back_prop=False is deprecated. Consider using tf.stop_gradient instead.\n",
            "Instead of:\n",
            "results = tf.while_loop(c, b, vars, back_prop=False)\n",
            "Use:\n",
            "results = tf.nest.map_structure(tf.stop_gradient, tf.while_loop(c, b, vars))\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 40/40 [07:00\u003c00:00, 10.52s/it]\n"
          ]
        }
      ],
      "source": [
        "learning_rate = tf.Variable(LR, trainable=False)\n",
        "optimizer = snt.optimizers.Adam(learning_rate)\n",
        "\n",
        "for epoch in tqdm.trange(NUM_EPOCHS // 2):\n",
        "  base_samples, transformed_samples = get_samples()\n",
        "  transformed_grid = get_transformed_grid()\n",
        "  evaluation_samples.append(\n",
        "      (base_samples, transformed_samples, transformed_grid))\n",
        "  for batch in moons_ds:\n",
        "    _ = train_step(optimizer, batch)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "colab_type": "code",
        "id": "G2sVjn77XiJt",
        "outputId": "44d9848e-f04c-4c5c-b760-8db19f405413"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "\u003cFigure size 1152x432 with 4 Axes\u003e"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "panel_id = -1\n",
        "panel_data = evaluation_samples[panel_id]\n",
        "fig, axarray = plt.subplots(\n",
        "  1, 4, figsize=(16, 6))\n",
        "plot_panel(grid, panel_data[0], panel_data[2], panel_data[1], moons, axarray)\n",
        "plt.tight_layout()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "EaXzmMliXiQ8"
      },
      "source": [
        "Training it for longer with learning rate results in further improvements."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "fIRkQ-33XsSH"
      },
      "source": [
        "Not convered in this example, FFJORD bijector supports hutchinson's stochastic trace estimation. The particular estimator can be provided via `trace_augmentation_fn`. Similarly alternative integrators can be used by defining custom `ode_solve_fn`. "
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "FFJORD Demo",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
